






"""simple docstring"""







import io
import json
import unittest

from parameterized import parameterized

from transformers import FSMTForConditionalGeneration, FSMTTokenizer
from transformers.testing_utils import get_tests_dir, require_torch, slow, torch_device
from utils import calculate_bleu


_UpperCamelCase	     =    get_tests_dir() + """/test_data/fsmt/fsmt_val_data.json"""
with io.open(filename, """r""", encoding="""utf-8""") as f:
  _UpperCamelCase	     =    json.load(f)







@require_torch
class    lowerCamelCase__   (			unittest.TestCase					):






    def 							_UpperCamelCase				(					self      ,A   ):
          return FSMTTokenizer.from_pretrained(lowerCAmelCase__   )






    def 							_UpperCamelCase				(					self      ,A   ):
          UpperCAmelCase							     =			FSMTForConditionalGeneration.from_pretrained(lowerCAmelCase__   ).to(lowerCAmelCase__   )
          if torch_device == "cuda":
                model.half()
          return model






    @parameterized.expand(
        [
            ["""en-ru""", 26.0],
            ["""ru-en""", 22.0],
            ["""en-de""", 22.0],
            ["""de-en""", 29.0],
        ]   )
    @slow
    def 							_UpperCamelCase				(					self      ,A      ,A   ):
          UpperCAmelCase							     =			F'''facebook/wmt19-{pair}'''
          UpperCAmelCase							     =			self.get_tokenizer(lowerCAmelCase__   )
          UpperCAmelCase							     =			self.get_model(lowerCAmelCase__   )

          UpperCAmelCase							     =			bleu_data[pair]["""src"""]
          UpperCAmelCase							     =			bleu_data[pair]["""tgt"""]

          UpperCAmelCase							     =			tokenizer(lowerCAmelCase__      ,return_tensors="""pt"""      ,truncation=lowerCAmelCase__      ,padding="""longest"""   ).to(lowerCAmelCase__   )
          UpperCAmelCase							     =			model.generate(
              input_ids=batch.input_ids      ,num_beams=8      ,)
          UpperCAmelCase							     =			tokenizer.batch_decode(
              lowerCAmelCase__      ,skip_special_tokens=lowerCAmelCase__      ,clean_up_tokenization_spaces=lowerCAmelCase__   )
          UpperCAmelCase							     =			calculate_bleu(lowerCAmelCase__      ,lowerCAmelCase__   )
          print(lowerCAmelCase__   )
          self.assertGreaterEqual(scores["""bleu"""]      ,lowerCAmelCase__   )




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__a    :	Tuple         =    {
    """configuration_luke""": ["""LUKE_PRETRAINED_CONFIG_ARCHIVE_MAP""", """LukeConfig"""],
    """tokenization_luke""": ["""LukeTokenizer"""],
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__a    :	Dict         =    [
		    """LUKE_PRETRAINED_MODEL_ARCHIVE_LIST""",
		    """LukeForEntityClassification""",
		    """LukeForEntityPairClassification""",
		    """LukeForEntitySpanClassification""",
		    """LukeForMultipleChoice""",
		    """LukeForQuestionAnswering""",
		    """LukeForSequenceClassification""",
		    """LukeForTokenClassification""",
		    """LukeForMaskedLM""",
		    """LukeModel""",
		    """LukePreTrainedModel""",
		]


if TYPE_CHECKING:
		from .configuration_luke import LUKE_PRETRAINED_CONFIG_ARCHIVE_MAP, LukeConfig
		from .tokenization_luke import LukeTokenizer

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_luke import (
				    LUKE_PRETRAINED_MODEL_ARCHIVE_LIST,
				    LukeForEntityClassification,
				    LukeForEntityPairClassification,
				    LukeForEntitySpanClassification,
				    LukeForMaskedLM,
				    LukeForMultipleChoice,
				    LukeForQuestionAnswering,
				    LukeForSequenceClassification,
				    LukeForTokenClassification,
				    LukeModel,
				    LukePreTrainedModel,
				)

else:
		import sys

		__a    :	Any         =    _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)